Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "142" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 27 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 27 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460010 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.598858 | 16.415597 | -0.909338 | 12.644154 | 1.454927 | 10.437468 | 16.591985 | 2.299314 | 0.6227 | 0.0501 | 0.4870 | nan | nan |
| 2460009 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.249128 | 15.107604 | -0.949877 | 13.920161 | 1.234292 | 8.825120 | 20.546539 | 2.406980 | 0.6228 | 0.0504 | 0.4880 | nan | nan |
| 2460008 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.320421 | 18.568174 | -1.035015 | 15.321342 | 1.038117 | 7.756742 | 6.047427 | 5.559762 | 0.6667 | 0.0536 | 0.5192 | nan | nan |
| 2460007 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.963894 | 13.905329 | -0.529303 | 11.983786 | 1.598572 | 7.197182 | 14.908117 | 2.614054 | 0.6311 | 0.0502 | 0.4873 | nan | nan |
| 2459999 | RF_maintenance | 0.00% | 99.25% | 99.58% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.2570 | 0.1704 | 0.2077 | nan | nan |
| 2459998 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.369642 | 11.705187 | -0.483678 | 10.144930 | 2.700625 | 10.204106 | 23.118349 | 1.841567 | 0.6250 | 0.0436 | 0.5158 | nan | nan |
| 2459997 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.301240 | 12.778111 | -0.645516 | 10.899329 | 1.430996 | 9.592119 | 34.584824 | 3.078922 | 0.6353 | 0.0493 | 0.5265 | nan | nan |
| 2459996 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.156022 | 13.765669 | -0.535953 | 13.341237 | 1.119737 | 9.227276 | 15.480873 | 1.555955 | 0.6474 | 0.0481 | 0.5272 | nan | nan |
| 2459995 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.523807 | 14.007642 | -0.885245 | 12.540608 | 2.608369 | 9.470772 | 16.285690 | 1.138255 | 0.6283 | 0.0533 | 0.5048 | nan | nan |
| 2459994 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.545508 | 13.574923 | -0.579602 | 10.984796 | 2.199245 | 9.547638 | 13.833971 | 1.845404 | 0.6209 | 0.0461 | 0.4988 | nan | nan |
| 2459993 | RF_maintenance | 100.00% | 0.00% | 98.65% | 0.00% | - | - | 1.853595 | 12.519218 | -0.776154 | 10.187067 | 2.455601 | 10.929086 | 13.464073 | 2.392706 | 0.6049 | 0.0391 | 0.4786 | nan | nan |
| 2459991 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.922869 | 15.793636 | -0.577187 | 10.779729 | 2.288659 | 10.724087 | 18.165699 | 0.800097 | 0.6416 | 0.0442 | 0.5314 | nan | nan |
| 2459990 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.339833 | 12.995851 | -0.454022 | 10.472556 | 3.111058 | 11.035735 | 27.945670 | 0.712955 | 0.6380 | 0.0476 | 0.5195 | nan | nan |
| 2459989 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.429893 | 13.179817 | -0.417329 | 9.573213 | 2.979499 | 9.269421 | 23.041763 | 0.803117 | 0.6287 | 0.0430 | 0.5132 | nan | nan |
| 2459988 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 2.176444 | 15.428358 | -0.592724 | 10.764141 | 3.085513 | 13.221599 | 18.605401 | 0.575794 | 0.6325 | 0.0435 | 0.5162 | nan | nan |
| 2459987 | RF_maintenance | 100.00% | 0.00% | 99.46% | 0.00% | - | - | 1.721564 | 12.920023 | -0.706702 | 10.618807 | 1.842219 | 7.979937 | 17.181857 | 2.534595 | 0.6430 | 0.0484 | 0.5258 | nan | nan |
| 2459986 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 2.266101 | 15.870448 | -0.711982 | 11.461019 | 2.568363 | 11.244842 | 9.293835 | 9.609154 | 0.6626 | 0.0466 | 0.5147 | nan | nan |
| 2459985 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.456912 | 14.300886 | -0.770831 | 10.675858 | 1.680094 | 8.598208 | 20.549597 | 1.926432 | 0.6428 | 0.0460 | 0.5265 | nan | nan |
| 2459984 | RF_maintenance | 100.00% | 0.00% | 98.27% | 0.00% | - | - | 1.296920 | 13.785705 | -0.921792 | 11.057506 | 0.426138 | 12.123729 | 12.238915 | 3.592015 | 0.6579 | 0.0531 | 0.5266 | nan | nan |
| 2459983 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.926935 | 13.470395 | -0.551626 | 10.473226 | 1.550687 | 11.137227 | 11.481686 | 6.155150 | 0.6607 | 0.0482 | 0.5145 | nan | nan |
| 2459982 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 0.093931 | 11.339710 | -0.186002 | 8.926253 | 1.919032 | 5.292733 | 2.248725 | 3.237823 | 0.7141 | 0.0477 | 0.5332 | nan | nan |
| 2459981 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.552862 | 12.412368 | -0.622720 | 11.145287 | 2.438406 | 12.343256 | 26.511627 | 0.898424 | 0.6330 | 0.0475 | 0.5138 | nan | nan |
| 2459980 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.235067 | 11.909353 | -0.736380 | 10.181780 | 2.077859 | 10.789878 | 2.223898 | 5.275203 | 0.6793 | 0.0492 | 0.5220 | nan | nan |
| 2459979 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.489029 | 12.473747 | -0.677673 | 9.529898 | 2.501544 | 10.095289 | 26.795730 | 0.915068 | 0.6275 | 0.0435 | 0.5118 | nan | nan |
| 2459978 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.767233 | 12.687495 | -0.715527 | 10.258069 | 2.713999 | 10.972449 | 27.506670 | 1.042067 | 0.6275 | 0.0418 | 0.5194 | nan | nan |
| 2459977 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.654635 | 13.435796 | -0.738445 | 10.106463 | 1.786997 | 11.285671 | 17.598779 | 1.034453 | 0.5951 | 0.0490 | 0.4832 | nan | nan |
| 2459976 | RF_maintenance | 100.00% | 0.00% | 100.00% | 0.00% | - | - | 1.408920 | 12.895878 | -0.672527 | 10.534059 | 3.331777 | 10.854490 | 22.924542 | 1.182155 | 0.6325 | 0.0432 | 0.5160 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 16.591985 | 1.598858 | 16.415597 | -0.909338 | 12.644154 | 1.454927 | 10.437468 | 16.591985 | 2.299314 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 20.546539 | 0.249128 | 15.107604 | -0.949877 | 13.920161 | 1.234292 | 8.825120 | 20.546539 | 2.406980 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | nn Shape | 18.568174 | 18.568174 | 1.320421 | 15.321342 | -1.035015 | 7.756742 | 1.038117 | 5.559762 | 6.047427 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 14.908117 | 1.963894 | 13.905329 | -0.529303 | 11.983786 | 1.598572 | 7.197182 | 14.908117 | 2.614054 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 23.118349 | 1.369642 | 11.705187 | -0.483678 | 10.144930 | 2.700625 | 10.204106 | 23.118349 | 1.841567 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 34.584824 | 1.301240 | 12.778111 | -0.645516 | 10.899329 | 1.430996 | 9.592119 | 34.584824 | 3.078922 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 15.480873 | 0.156022 | 13.765669 | -0.535953 | 13.341237 | 1.119737 | 9.227276 | 15.480873 | 1.555955 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 16.285690 | 0.523807 | 14.007642 | -0.885245 | 12.540608 | 2.608369 | 9.470772 | 16.285690 | 1.138255 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 13.833971 | 1.545508 | 13.574923 | -0.579602 | 10.984796 | 2.199245 | 9.547638 | 13.833971 | 1.845404 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 13.464073 | 1.853595 | 12.519218 | -0.776154 | 10.187067 | 2.455601 | 10.929086 | 13.464073 | 2.392706 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 18.165699 | 1.922869 | 15.793636 | -0.577187 | 10.779729 | 2.288659 | 10.724087 | 18.165699 | 0.800097 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 27.945670 | 12.995851 | 1.339833 | 10.472556 | -0.454022 | 11.035735 | 3.111058 | 0.712955 | 27.945670 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 23.041763 | 13.179817 | 1.429893 | 9.573213 | -0.417329 | 9.269421 | 2.979499 | 0.803117 | 23.041763 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 18.605401 | 15.428358 | 2.176444 | 10.764141 | -0.592724 | 13.221599 | 3.085513 | 0.575794 | 18.605401 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 17.181857 | 1.721564 | 12.920023 | -0.706702 | 10.618807 | 1.842219 | 7.979937 | 17.181857 | 2.534595 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | nn Shape | 15.870448 | 15.870448 | 2.266101 | 11.461019 | -0.711982 | 11.244842 | 2.568363 | 9.609154 | 9.293835 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 20.549597 | 14.300886 | 1.456912 | 10.675858 | -0.770831 | 8.598208 | 1.680094 | 1.926432 | 20.549597 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | nn Shape | 13.785705 | 1.296920 | 13.785705 | -0.921792 | 11.057506 | 0.426138 | 12.123729 | 12.238915 | 3.592015 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | nn Shape | 13.470395 | 1.926935 | 13.470395 | -0.551626 | 10.473226 | 1.550687 | 11.137227 | 11.481686 | 6.155150 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | nn Shape | 11.339710 | 0.093931 | 11.339710 | -0.186002 | 8.926253 | 1.919032 | 5.292733 | 2.248725 | 3.237823 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 26.511627 | 12.412368 | 1.552862 | 11.145287 | -0.622720 | 12.343256 | 2.438406 | 0.898424 | 26.511627 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | nn Shape | 11.909353 | 11.909353 | 1.235067 | 10.181780 | -0.736380 | 10.789878 | 2.077859 | 5.275203 | 2.223898 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 26.795730 | 1.489029 | 12.473747 | -0.677673 | 9.529898 | 2.501544 | 10.095289 | 26.795730 | 0.915068 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 27.506670 | 12.687495 | 1.767233 | 10.258069 | -0.715527 | 10.972449 | 2.713999 | 1.042067 | 27.506670 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 17.598779 | 1.654635 | 13.435796 | -0.738445 | 10.106463 | 1.786997 | 11.285671 | 17.598779 | 1.034453 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | N13 | RF_maintenance | ee Temporal Discontinuties | 22.924542 | 12.895878 | 1.408920 | 10.534059 | -0.672527 | 10.854490 | 3.331777 | 1.182155 | 22.924542 |